Intro

Basic analysis of Vancouver weather patterns:

Data Collection

Data is collected from Govt of Canada website:

  1. search: http://climate.weather.gc.ca/historical_data/search_historic_data_e.html
  2. specify station as containing ‘vancouver harbour’
  3. select ‘daily’, select ‘year’
  4. ‘Go’ goes to a page to access results for year

(process is automated in ‘van-weather-import.R’)

## [1] "Winter" "Spring" "Summer" "Fall"

Earliest date: 1998-01-01
Most recent date: 2019-07-26

Check data structure:

## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 7874 obs. of  10 variables:
##  $ Month       : Factor w/ 12 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Season      : Factor w/ 4 levels "Winter","Spring",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Season.Yr   : num  1998 1998 1998 1998 1998 ...
##  $ Date        : Date, format: "1998-01-01" "1998-01-02" ...
##  $ Year        : Factor w/ 22 levels "1998","1999",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Day         : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ Max.Temp    : num  8.7 4.2 1.7 2.8 5.7 7 7.3 7.9 4 3.4 ...
##  $ Min.Temp    : num  3 -1.1 -2.2 -0.9 2.3 3.6 4 1.3 0.3 -1.7 ...
##  $ Mean.Temp   : num  5.9 1.6 -0.3 1 4 5.3 5.7 4.6 2.2 0.9 ...
##  $ Total.Precip: num  5.6 0.6 0.2 6 6.2 3.8 0 0 0 0.2 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Month = col_double(),
##   ..   Season = col_character(),
##   ..   Season.Yr = col_double(),
##   ..   Date = col_date(format = ""),
##   ..   Year = col_double(),
##   ..   Day = col_double(),
##   ..   Max.Temp = col_double(),
##   ..   Min.Temp = col_double(),
##   ..   Mean.Temp = col_double(),
##   ..   Total.Precip = col_double()
##   .. )

Earliest date: 1998-01-01
Most recent date: 2019-07-26

Check Data Relationships

  • uses PerformanceAnalytics package

Precipitation

Monthly precipitation data: Averages

Data by Month - ranked in order of highest precipitation

Monthly Preciptiation: Ranges

Current Month Precipitation

Monthly Comparison YoY

For each month, what has been the pattern in precipitation over the years?

Seasonal Precipitation

Season definitions:

  • Winter: Dec, Jan, Feb
  • Spring: Mar, Apr, May
  • Summer: Jun, Jul, Aug
  • Fall: Sep, Oct, Nov

Note: the year for a winter season is applied to year at end of season. So winter from Dec 2018 to Feb 2019 is considered winter of 2019.

Seasonal Precipitation Annual Ranking

  • Need separate charts because each season ranked individually
  • Line = median

Temperature

Ave. Annual Temperature

Linear model of annual temperature change

## 
## Call:
## lm(formula = mean.mean ~ ynum, data = vw.temp.yr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.50567 -0.22162 -0.09896  0.50017  1.26771 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  6.060969  53.358353   0.114    0.911
## ynum         0.002595   0.026566   0.098    0.923
## 
## Residual standard error: 0.7905 on 20 degrees of freedom
## Multiple R-squared:  0.0004769,  Adjusted R-squared:  -0.0495 
## F-statistic: 0.009542 on 1 and 20 DF,  p-value: 0.9232

Model interpretation:

  • Average daily temperature changing 0.0025951 degrees each yr.
  • P-value for statistical significance: 0.9231563
  • Temperature change is not statistically significant over this period

Look at Average Daily Maximum and Minimums for each Year

Linear modelling for max and min

## 
## Call:
## lm(formula = mean.max ~ ynum, data = vw.temp.yr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.99876 -0.22113  0.00135  0.50616  1.41281 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.963644  60.774591  -0.082    0.936
## ynum         0.009613   0.030259   0.318    0.754
## 
## Residual standard error: 0.9004 on 20 degrees of freedom
## Multiple R-squared:  0.005021,   Adjusted R-squared:  -0.04473 
## F-statistic: 0.1009 on 1 and 20 DF,  p-value: 0.754

Model interpretation:

  • Average daily MAX temperature changing 0.0096126 degrees each yr.
  • P-value for statistical significance: 0.7540195
  • Temperature change is not statistically significant over this period
## 
## Call:
## lm(formula = mean.min ~ ynum, data = vw.temp.yr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.00544 -0.30495 -0.08373  0.43447  1.07114 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.574971  47.612167   0.243    0.810
## ynum        -0.001706   0.023705  -0.072    0.943
## 
## Residual standard error: 0.7054 on 20 degrees of freedom
## Multiple R-squared:  0.0002589,  Adjusted R-squared:  -0.04973 
## F-statistic: 0.00518 on 1 and 20 DF,  p-value: 0.9433

Model interpretation:

  • Average daily MIN temperature changing -0.0017062 degrees each yr.
  • P-value for statistical significance: 0.9433378
  • Temperature change is not statistically significant over this period

Monthly average temperature

Monthly Temperatures: Ranges

Monthly Temp. Comparison YoY

For each month, what has been the pattern in ave. temperature over the years?

Seasonal Temperatures

Season definitions:

  • Winter: Dec, Jan, Feb
  • Spring: Mar, Apr, May
  • Summer: Jun, Jul, Aug
  • Fall: Sep, Oct, Nov

Note: the year for a winter season is applied to year at end of season. So winter from Dec 2018 to Feb 2019 is considered winter of 2019.